Language model features are easy to demo and hard to trust. The difference between the two is evaluation: a repeatable way to measure whether the system does what you expect, across the cases that matter.
We start every AI feature with a golden dataset — a few hundred real examples with expected outcomes, reviewed by domain experts. Each change to prompts, retrieval or models runs against this set in CI, and scores are tracked over time like any other quality metric.
For generative outputs we combine deterministic checks (format, required fields, forbidden content) with model-graded rubrics, calibrated against human judgement on a sample. Low-confidence cases go to a human review queue rather than to the customer.
In production, we log inputs and outputs with consent, monitor drift in the kinds of requests users make, and feed reviewed failures back into the golden set. The harness becomes more valuable every week the feature is live.